/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.mllib;

// $example on$

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaDoubleRDD;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.recommendation.ALS;
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
import org.apache.spark.mllib.recommendation.Rating;
import scala.Tuple2;
// $example off$

public class JavaRecommendationExample {
    public static void main(String[] args) {
        // $example on$
        SparkConf conf = new SparkConf().setAppName("Java Collaborative Filtering Example");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        
        // Load and parse the data
        String path = "data/mllib/als/demo3.data";
        JavaRDD<String> data = jsc.textFile(path);
        JavaRDD<Rating> ratings = data.map(
                new Function<String, Rating>() {
                    public Rating call(String s) {
                        String[] sarray = s.split(",");
                        return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]),
                                Double.parseDouble(sarray[2]));
                    }
                }
        );
        
        // Build the recommendation model using ALS
        int rank = 10;
        int numIterations = 10;
        MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);
        
        // Evaluate the model on rating data
        JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
                new Function<Rating, Tuple2<Object, Object>>() {
                    public Tuple2<Object, Object> call(Rating r) {
                        return new Tuple2<Object, Object>(r.user(), r.product());
                    }
                }
        );
        JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
                model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
                        new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
                            public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r) {
                                return new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating());
                            }
                        }
                ));
        JavaRDD<Tuple2<Double, Double>> ratesAndPreds =
                JavaPairRDD.fromJavaRDD(ratings.map(
                        new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
                            public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r) {
                                return new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating());
                            }
                        }
                )).join(predictions).values();
        double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
                new Function<Tuple2<Double, Double>, Object>() {
                    public Object call(Tuple2<Double, Double> pair) {
                        Double err = pair._1() - pair._2();
                        return err * err;
                    }
                }
        ).rdd()).mean();
        System.out.println("Mean Squared Error = " + MSE);
        
        // Save and load model
        model.save(jsc.sc(), "target/tmp/myCollaborativeFilter");
        MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(jsc.sc(),
                "target/tmp/myCollaborativeFilter");
        // $example off$
        
        jsc.stop();
    }
}
